Linkens DA - Search Results

1

Application of artificial intelligence to the management of urological cancer.

Abbod MF, Catto JW, Linkens DA, Hamdy FC. Abbod MF, et al. Among authors: linkens da. J Urol. 2007 Oct;178(4 Pt 1):1150-6. doi: 10.1016/j.juro.2007.05.122. Epub 2007 Aug 14. J Urol. 2007. PMID: 17698099 Review.

2

Artificial intelligence and bladder cancer arrays.

Wild PJ, Catto JW, Abbod MF, Linkens DA, Herr A, Pilarsky C, Wissmann C, Stoehr R, Denzinger S, Knuechel R, Hamdy FC, Hartmann A. Wild PJ, et al. Among authors: linkens da. Verh Dtsch Ges Pathol. 2007;91:308-19. Verh Dtsch Ges Pathol. 2007. PMID: 18314629

3

Comparative study of intelligent models for the prediction of bladder cancer progression.

Abbod MF, Linkens DA, Catto JW, Hamdy FC. Abbod MF, et al. Among authors: linkens da. Oncol Rep. 2006;15 Spec no.:1019-22. doi: 10.3892/or.15.4.1019. Oncol Rep. 2006. PMID: 16525693

4

The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression.

Catto JW, Abbod MF, Wild PJ, Linkens DA, Pilarsky C, Rehman I, Rosario DJ, Denzinger S, Burger M, Stoehr R, Knuechel R, Hartmann A, Hamdy FC. Catto JW, et al. Among authors: linkens da. Eur Urol. 2010 Mar;57(3):398-406. doi: 10.1016/j.eururo.2009.10.029. Epub 2009 Nov 6. Eur Urol. 2010. PMID: 19913990 Free article.

5

Promoter hypermethylation identifies progression risk in bladder cancer.

Yates DR, Rehman I, Abbod MF, Meuth M, Cross SS, Linkens DA, Hamdy FC, Catto JW. Yates DR, et al. Among authors: linkens da. Clin Cancer Res. 2007 Apr 1;13(7):2046-53. doi: 10.1158/1078-0432.CCR-06-2476. Clin Cancer Res. 2007. PMID: 17404085

6

Neuro-fuzzy modeling: an accurate and interpretable method for predicting bladder cancer progression.

Catto JW, Abbod MF, Linkens DA, Hamdy FC. Catto JW, et al. Among authors: linkens da. J Urol. 2006 Feb;175(2):474-9. doi: 10.1016/S0022-5347(05)00246-6. J Urol. 2006. PMID: 16406976

7

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

Catto JW, Linkens DA, Abbod MF, Chen M, Burton JL, Feeley KM, Hamdy FC. Catto JW, et al. Among authors: linkens da. Clin Cancer Res. 2003 Sep 15;9(11):4172-7. Clin Cancer Res. 2003. PMID: 14519642

8

Neural networks and nonlinear regression modelling and control of depth of anaesthesia for spontaneously breathing and ventilated patients.

Rehman HU, Linkens DA, Asbury AJ. Rehman HU, et al. Among authors: linkens da. Comput Methods Programs Biomed. 1993 Aug;40(4):227-47. doi: 10.1016/0169-2607(93)90009-a. Comput Methods Programs Biomed. 1993. PMID: 8261771

9

Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.

Mason DG, Ross JJ, Edwards ND, Linkens DA, Reilly CS. Mason DG, et al. Among authors: linkens da. Comput Biomed Res. 1999 Jun;32(3):187-97. doi: 10.1006/cbmr.1999.1507. Comput Biomed Res. 1999. PMID: 10356301

10

The intelligent systems in biomedicine laboratory in the Department of Automatic Control and Systems Engineering at the University of Sheffield, UK.

Linkens DA, Mahfouf M. Linkens DA, et al. Artif Intell Med. 2001 Jan-Mar;21(1-3):171-6. doi: 10.1016/s0933-3657(00)00082-8. Artif Intell Med. 2001. PMID: 11154882

Save citations to file

Email citations

Send citations to clipboard

Add to Collections

Add to My Bibliography

Create a file for external citation management software

Your saved search

Your RSS Feed

Search Page

Search Results